Estimating 3D human pose and shape from monocular images is a crucial task for various applications such as performance retargeting, virtual avatars, and human action recognition.
It is a fundamentally challenging problem due to the depth ambiguity and the complex nature of human appearances that vary with articulation, clothing, lighting, viewpoint, and occlusions.
To represent the complicated 3D human bodies via compact parameters, model-based methods like SMPL [1]} have been widely used in the community.
However, SMPL parameters represent human bodies in a holistic manner, causing their limited flexibility to fit real-world images faithfully via direct regression.
More importantly, the regression-based methods tend to fail when a human body is not fully visible in the image, e.g., occluded or out of frame [2]}.
In this work, we aim to learn human body estimation that is faithful to the input images and robust to partial-body cases.
